Malware homology analysis method based on family gene similarity

Xiaoying Zou; Feng Li; Rui Wang; Yawen Zhao; Kaijie Liu; Jianyi Liu

doi:10.1117/12.3025846

8 April 2024 Malware homology analysis method based on family gene similarity

Xiaoying Zou, Feng Li, Rui Wang, Yawen Zhao, Kaijie Liu, Jianyi Liu

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 1309047 (2024) https://doi.org/10.1117/12.3025846
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Existing natural language processing-based methods for analyzing malware suffer from the inability to effectively extract features, resulting in low homology analysis accuracy and analytical errors. This paper proposes a malware homology analysis model based on family gene similarity. It employs the deep unsupervised clustering algorithm DEC to cluster assembly code function vectors, effectively extracting gene clusters of malware families. The LightGBM algorithm is then used for homology analysis. Experimental results demonstrate that this method outperforms other homology analysis methods and can effectively handle unknown threats.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoying Zou, Feng Li, Rui Wang, Yawen Zhao, Kaijie Liu, and Jianyi Liu "Malware homology analysis method based on family gene similarity", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 1309047 (8 April 2024); https://doi.org/10.1117/12.3025846

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